ArticleBioengineering (Basel, Switzerland)2024
Unsupervised Segmentation of Knee Bone Marrow Edema-like Lesions Using Conditional Generative Models.
Article in Bioengineering (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Multi-label Ensemble Model for Knee Joint Anatomy and Lesion Segmentation: Segmentation of Clinical Images With Ensembling, Morphology, and Attention (SCEMA).Magnetic resonance in medicine · 2026Article
- Clinical validation of fully automated (peri-)articular tissue analysis for assessing osteoarthritis progression: A narrative review.Osteoarthritis and cartilage open · 2026Review
- A fully automatic knee subregion segmentation network based on tissue segmentation and anatomical geometry.Scientific reports · 2025Article
- Deep learning detects retropharyngeal edema on MRI in patients with acute neck infections.European radiology experimental · 2025Article
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Authors and funding
11 authors.
Funding
Abstract
Bone marrow edema-like lesions (BMEL) in the knee have been linked to the symptoms and progression of osteoarthritis (OA), a highly prevalent disease with profound public health implications. Manual and semi-automatic segmentations of BMELs in magnetic resonance images (MRI) have been used to quantify the significance of BMELs. However, their utilization is hampered by the labor-intensive and time-consuming nature of the process as well as by annotator bias, especially since BMELs exhibit various sizes and irregular shapes with diffuse signal that lead to poor intra- and inter-rater reliability. In this study, we propose a novel unsupervised method for fully automated segmentation of BMELs that leverages conditional diffusion models, multiple MRI sequences that have different contrast of BMELs, and anomaly detection that do not rely on costly and error-prone annotations. We also analyze BMEL segmentation annotations from multiple experts, reporting intra-/inter-rater variability and setting better benchmarks for BMEL segmentation performance.
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Registered trials
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